key: cord-0845430-c4ihkcyr
authors: Harris, Jeffrey E.
title: COVID-19 Incidence and Hospitalization During the Delta Surge Were Inversely Related to Vaccination Coverage Among the Most Populous U.S. Counties
date: 2021-11-30
journal: Health Policy Technol
DOI: 10.1016/j.hlpt.2021.100583
sha: 1e0799b3ef5e1b3b75fe6a3b683a7f3c5191f0ee
doc_id: 845430
cord_uid: c4ihkcyr

OBJECTIVE: We tested whether COVID-19 incidence and hospitalization rates during the Delta surge were inversely related to vaccination coverage among the 112 most populous counties in the United States, comprising 44 percent of the country's total population. METHODS: We measured vaccination coverage as the percent of the county population fully vaccinated as of July 15, 2021. We measured COVID-19 incidence as the number of confirmed cases per 100,000 population during the 14-day period ending August 12, 2021 and hospitalization rates as the number of confirmed COVID-19 admissions per 100,000 population during the same 14-day period. RESULTS: In log-linear regression models, a 10-percentage-point increase in vaccination coverage was associated with a 28.3% decrease in COVID-19 incidence (95% confidence interval, 16.8 - 39.7%), a 44.9 percent decrease in the rate of COVID-19 hospitalization (95% CI, 28.8 - 61.0%), and a 16.6% decrease in COVID-19 hospitalizations per 100 cases (95% CI, 8.4 - 24.8%). Inclusion of demographic covariables, as well as county-specific diabetes prevalence, did not weaken the observed inverse relationship with vaccination coverage. A significant inverse relationship between vaccination coverage and COVID-19 deaths per 100,000 during August 20 – September 16 was also observed. The cumulative incidence of COVID-19 through June 30, 2021, a potential indicator of acquired immunity due to past infection, had no significant relation to subsequent case incidence or hospitalization rates in August. CONCLUSION: Higher vaccination coverage was associated not only with significantly lower COVID-19 incidence during the Delta surge, but also significantly less severe cases of the disease. Public Interest Summary We tested whether COVID-19 incidence and hospitalization rates during the Delta variant-related surge were inversely related to vaccination coverage among the 112 most populous counties in the United States, together comprising 44 percent of the country's total population. A 10-percentage-point increase in vaccination coverage was associated with a 28.3% decrease in COVID-19 incidence, a 44.9 percent decrease in the rate of COVID-19 hospitalization, and a 16.6% decrease in COVID-19 hospitalizations per 100 cases. Inclusion of demographic covariables, as well as county-specific diabetes prevalence, did not weaken the observed inverse relationship with vaccination coverage. A significant inverse relationship between vaccination coverage and COVID-19 deaths per 100,000 during August 20 – September 16 was also observed. Higher vaccination coverage was associated not only with significantly lower COVID-19 incidence during the Delta surge, but also significantly less severe cases of the disease.

By the second week of July 2021, the fast-spreading Delta variant had been detected in more than 99 percent of all SARS-CoV-2 viral isolates reported in the United States. 1 While the Delta-driven surge in COVID-19 cases in the U.S. initially appeared to have been concentrated in places with relatively low vaccination rates, 2-4 by early August there were reports of emerging hot spots in highly vaccinated parts of the country. 5 By mid-August, breakthrough infections in fully vaccinated individuals, 6 in part the result of a diminution over time in vaccine effectiveness, 7 8 had risen in some places to as high as 30 percent of all reported cases. 9 Fully vaccinated individuals, once infected with the Delta variant, were found to be capable of transmitting their infections to others, 10 though their viral load and duration of infectivity were found to be lower than that of unvaccinated infected individuals. 11 In view of these developments, we conducted an observational, cross-sectional analysis of the relation between vaccination coverage and COVID-19 disease rates among counties in the United States during the Delta-driven surge. Specifically, we tested whether COVID-19 incidence and hospitalization rates during the two weeks ending August 12 were inversely related to the percentage of the population fully vaccinated by mid-July 2021. To avoid comparing small rural counties with large urban centers, we concentrated on the 112 largest counties, each with a population over 600,000, and together with a combined total population of 147 million persons, or about 44 percent of the entire U.S. population.

Principal Analyses. Our data derive principally from the COVID-19 Community Profile Report maintained at healthdata.gov. 12 The Counties tab in the spreadsheet for 8/12/2021 gave the incidence of COVID-19 cases per 100,000 population during the most recent and the previous 7-day periods, from which we calculated the 14-day cumulative incidence. The spreadsheets for 8/5/2021 and 8/12/2021 gave the numbers of confirmed COVID-19 hospitalizations for the two previous 7-day periods, from which we computed county-specific 14-day hospital admission rates per 100,000. We also computed the number of COVID-19 hospital admissions per 100 cases, which we defined as 100 times the hospital admission rate divided by the incidence rate.

HLPT-D-21-00370 R2 9-Nov-2021 5 We similarly relied on the Counties tab in the spreadsheet for 7/15/2021 to extract the county-specific percentage of the population fully vaccinated as of that date. Since vaccination coverage for Texas was omitted from the Community Profile Report, we supplemented our database with state-specific data compiled by the Democrat and Chronicle as of 7/14/21. 13 These sources, taken together, provided us with one independent variablethe vaccination coverage in each county as of mid-Julyand three dependent variables -14-day COVID-19 incidence, 14-day COVID-19 hospital admission rates, and COVID-19 hospital admissions per 100 casesin each county for the period ending August 12. These variables together served as the basis of our principal regression analyses, described below.

Ancillary Analyses. In a series of ancillary analyses, we considered two additional dependent variables: (1) the test positivity rate, defined as the 14-day incidence of COVID-19 divided by the total number of polymerase chain reaction (PCR) diagnostic tests for COVID-19

during the same 14-day period ending 8/12/21; and (2) the COVID-19 death rate, defined as the cumulative number of deaths from COVID-19 per 100,000 population recorded during the 4week interval from 8/20 through 9/14/21. The data underlying the test positivity rate were derived from the 8/12/21 spreadsheet, while the data underlying the death rate were derived from the 8/19/21 and 9/14/21 spreadsheets of the COVID-19 Community Profile Report.

In our ancillary analyses, we also considered the following additional independent 15 16 Diabetes prevalence was derived from the CDC's Diabetes Atlas. 17 The remaining independent variables were derived from the 8/12/21 spreadsheet of the COVID-19 Community Profile Report.

Principal Analyses. In our principal analyses, we identified 112 counties with population at last 600,000. These counties are mapped in Fig. A1 in Appendix A and enumerated in the We first conducted a descriptive analysis of the data. To that end, we divided our study sample of 112 counties into 56 counties in the lower half and 56 counties in the upper half of the distribution of vaccination coverage. We computed the means for each of the three dependent variables in both the lower and upper halves and then relied on the t-test based upon unequal variances to assess differences in group means.

We then conducted a cross-sectional regression analysis of the sample of 112 counties, where each county constituted a distinct observation. We employed ordinary least squares (OLS) to estimate the parameters ( ) of the log-linear model log = , where is the dependent variable of interest in each county (that is, COVID-19 incidence, COVID-19 hospitalization rate, or the hospitalization-case ratio) and represents the corresponding vaccination coverage in that county. In our results below, we report these estimates as Model 1.

We also estimated the same log-linear model by population-weighted least squares (reported as Model 2) . We further estimated the model log = , where and , respectively, are binary parameters indicating whether the county was one of the 10 located in Florida or one of the 11 located in Texas (Model 3). We specifically focused on counties in these two large, populous states as they were reported to have especially high rates of infection and hospitalization during the Delta variant-driven surge. [18] [19] [20] [21] Ancillary Analyses. We conducted several ancillary analyses to test the robustness of our Fig. 3 shows that its hospitalization-to-case ratio, an indicator of case severity, is in line with its 58.4 percent vaccination coverage. The column corresponding to Model 2 in Table 2 shows insignificant changes in the estimated values of when we ran a population-weighted regression rather than ordinary least squares. The results in the column corresponding to Model 3 demonstrate that the estimates of remained significant even when we included the binary indicator variables for Florida and Texas. Table A3 in the Appendix displays the results of re-estimation of Models 1 through 3 on the alternative, expanded database of 138 counties with population 500,000. The results were virtually identical to those reported in Table 2 above. where the covariate was omitted. Hispanic had a significant positive effect on both COVID-19 incidence and the hospitalization rate. The SVI score and diabetes prevalence had significant positive effects on both the hospitalization rate and the hospitalizations per 100 cases.

Numerous factors could have contributed to the substantial scatter of the datapoints seen in Figs. 1 through 3 . In our ancillary analyses, we attempted to control for county-specific differences in demographic characteristics, as well as the prevalence of diabetes, a strong predictor of COVID-19 case severity. 22 Apart from these factors, it is possible that differences in public policies, including prohibition of mandates on vaccination and mask-wearing in schools and workplaces in certain states, may have been contributory. 23 A critical limitation of the current study is that the COVID-19 Community Profile Report, maintained at healthdata.gov, 12 does not provide a detailed breakdown of our county-specific data on vaccination coverage, COVID-19 incidence and hospitalization rates by age group. Still, the persistence of clearly detectable differences between low-and high-vaccination countieseven with the low statistics seen in the regression results in Table 2 points to an important, identifiable deterrent effect of vaccinations on disease spread during the Delta surge.

Our analysis focused on the most populous counties in the U.S., comprising 44.4% of the total population. We excluded less populous, rural counties, where transmission dynamics are likely to be quite different, 24 and where smaller population denominators tend to result in higher sampling variability. We thus avoided the pitfall of drawing biased conclusions from the study of rural and urban counties combined. 25 While our choice of a population cutoff of 600,000

inhabitants is necessarily arbitrary, our principal results remained unchanged when we expanded our database by lowering the cutoff to 500,000 (Appendix Table A3 ).

While there is evidence that as many as one-third of COVID-19 survivors have no detectable antibodies against SARS-CoV-2, 26 it is likely that those who experienced a sufficiently high viral load during their illness have acquired some degree of natural immunity.

In that case, we would expect to observe a protective effect of higher rates of past infection on COVID-19 incidence and hospitalizations, even taking vaccination coverage into account. Yet Table A4 ). Nor did its inclusion in our regression Model 4 attenuate the effect of vaccination coverage (Fig. 5) . These negative findings may be the result of the limited duration of naturally acquired immunity. 27 Counts of confirmed cases based on voluntary testing of symptomatic individuals are known to have significantly understated actual numbers of SARS-CoV-2 infections 28 29 This consideration at least raises the possibility that the extent of ascertainment of COVID-19

infections could be inversely correlated with a county's vaccination coverage. The respective parameter estimates of in Model 1 ( = + ) were -0.0283 when the dependent variable was the COVID-19 incidence rate (Table 2 ) and -0.0441 when the dependent variable was the test positivity rate ( Table 3 ). The fact that the former estimate of is algebraically greater than the latter implies that counties with higher vaccination coverage have performed more testing per capita. To maintain that an ascertainment bias is a valid explanation for the significant inverse relation seen in Fig. 1 , one would have to posit that counties with higher vaccination coverage have been more aggressive in testing uninfected individuals while somehow detecting fewer infected individuals.

Our finding that COVID-19 death rates are inversely related to vaccination coverage is consistent with our results on hospitalization rates. Still, there is a substantial, highly variable delay between initial diagnosis and death, with the mean lag time for the original coronavirus on the order of 16 days. 30 31 While the Delta variant appears to have a shorter incubation time from infection to symptoms, 32 the time from symptoms to death is less well characterized. We measured COVID-19 incidence and hospitalization during 7/30 -8/12/21, an observation interval starting two weeks after the mid-July cutoff date for ascertaining vaccination coverage.

To accommodate the variable delay in mortality, we measured subsequent deaths during 8/20 -9/16/21. However, we cannot be confident of a one-to-one mapping between cases diagnosed during 7/30 -8/12/21 and deaths that occurred during 8/20 -9/16/21.

Our scatterplots (Figs. 1 and 2 ) and regression results ( Table A4 ) fails to support the hypothesis that Florida's high rates of infection and 

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COVID-19 Incidence and Hospitalization Rates are Inversely Related to Vaccination Coverage Among the 112 Most Populous Counties in the United States